iTRPL：一种基于多智能体强化学习的智能可信RPL协议
iTRPL: An Intelligent and Trusted RPL Protocol based on Multi-Agent Reinforcement Learning
AI总结：
针对RPL协议易受内部攻击的问题，提出融合信任机制与多智能体强化学习的iTRPL框架，通过节点信任计算、奖惩聚合及ε-Greedy决策实现DODAG的自主安全优化，仿真验证其可随时间学习做出最优决策。
摘要：
低功耗有损网络路由协议（Routing Protocol for Low Power and Lossy Networks，RPL）是物联网网络中的事实路由标准。它支持节点协作并自主构建自组织网络，该网络由面向目的地的树状有向无环图（DODAG）建模。尽管RPL在工业和医疗领域得到广泛应用，但它易受内部攻击。尽管当前最先进的RPL确保只有经过认证的节点才能参与DODAG构建，但这类硬性安全措施仍不足以防范内部威胁，因此需要整合软性安全机制来支撑决策。本文提出iTRPL，这是一种智能的、基于行为的框架，融合信任机制以在DODAG内区分诚实节点和恶意节点。该框架还利用多智能体强化学习（MARL）对DODAG做出自主决策。框架支持父节点计算其子节点的信任值，并决定子节点是否可以加入DODAG；它跟踪子节点的行为、更新信任值、计算奖励（或惩罚）并共享给根节点。根节点聚合所有节点的奖励/惩罚，计算总体回报，并通过其ε-Greedy MARL模块决定未来是保留还是修改DODAG。基于仿真的性能评估表明，iTRPL能够随着时间推移学习做出最优决策。
英文摘要：
Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its $ε$-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
